Evidence map›Paper›PMID 32565637›Full record

ArticleIndian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine2020

Evaluation and Validation of Four Scoring Systems: the APACHE IV, SAPS III, MPM0 II, and ICMM in Critically Ill Cancer Patients.

Suhail S Siddiqui, Amit M Narkhede, Atul P Kulkarni, Natesh R Prabu, Harish K Chaudhari, Satish V Sarode, Jigeeshu V Divatia

Abstract read
In one paragraph

Article in Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Epidemiology, Clinical Characteristics, and Prognostic Factors in Critically Ill Patients with Hematolymphoid Malignancy.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2021
    Article
  3. Ideal Severity of Illness Scoring System for Critically Ill Cancer Patients: A Dream.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2020
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Suhail S SiddiquiDepartment of Critical Care Medicine, King George's Medical University, Lucknow, Uttar Pradesh, India.
Amit M NarkhedeDivision of Critical Care Medicine, Department of Anesthesia, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India.
Atul P KulkarniDepartment of Anesthesia, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India.
Natesh R PrabuDepartment of Critical Care Medicine, St John's Medical College, Bengaluru, Karnataka, India.
Harish K ChaudhariDepartment of Critical Care, Arneja Heart and Multispeciality Hospital, Nagpur, Maharashtra, India.
Satish V SarodeDivision of Critical Care Medicine, Department of Anesthesia, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India.
Jigeeshu V DivatiaDepartment of Anesthesia, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsTo evaluate and validate four severity-of-illness scores, acute physiology and chronic health evaluation IV (APACHE IV), simplified acute physiology score III (SAPS III), mortality probability models II at 0 hours (MPM0 II), and ICU cancer mortality model (ICMM), in a prospective cohort of critically ill cancer patients. MATERIALS AND

methodsSingle-center, prospective observational study performed in a 14-bedded combined medical-surgical ICU of a tertiary care cancer center of India, from July 2014 to November 2015. Score performance was judged by discrimination and calibration, using the area under receiver-operating characteristics (ROC) curve and Hosmer-Lemeshow goodness-of-fit test, respectively.

resultsA total of 431 patients were included in the study. Intensive care unit (ICU) and hospital mortality were 37.4% and 41.1%, respectively. The area under ROC curve for APACHE IV, SAPS III, MPM0 II, and ICMM were 0.73, 0.70, 0.67, and 0.67, respectively. Calibration as calculated by Hosmer-Lemeshow analysis type C statistics for APACHE IV, SAPS III, MPM0 II, and ICMM shows good calibration with Chi-square values of 5.32, 9.285, 9.873, and 9.855 and

conclusionAll the four models had moderate discrimination and good calibration. However, none of the mortality prediction models could accurately discriminate between survivors and nonsurvivors in our patients. HOW TO CITE THIS ARTICLE: Siddiqui SS, Narkhede AM, Kulkarni AP, Prabu NR, Chaudhari HK, Divatia JV,

Indexed as

Acute physiology and chronic health evaluation IVCancerIntensive care unit cancer mortality modelIntensive care unit mortalityIntensive care unit outcomeMortality probability models II at 0 hoursSeverity-of-illness scoring systemsSimplified acute physiology score 3

Identifiers

PMID32565637
PMCPMC7297244

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.